Overview

Dataset statistics

Number of variables13
Number of observations28080
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory104.0 B

Variable types

Numeric7
Categorical4
Text2

Alerts

periodo is uniformly distributedUniform
plan_precios_cuidados has 27842 (99.2%) zerosZeros
cust_request_qty has 5731 (20.4%) zerosZeros
cust_request_tn has 5731 (20.4%) zerosZeros
tn has 5731 (20.4%) zerosZeros

Reproduction

Analysis started2024-07-13 12:45:13.949006
Analysis finished2024-07-13 12:45:19.652266
Duration5.7 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

product_id
Real number (ℝ)

Distinct780
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20541.422
Minimum20001
Maximum21276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:19.725209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile20042.95
Q120238.75
median20511.5
Q320818.5
95-th percentile21167.05
Maximum21276
Range1275
Interquartile range (IQR)579.75

Descriptive statistics

Standard deviation353.76366
Coefficient of variation (CV)0.017221965
Kurtosis-1.0230288
Mean20541.422
Median Absolute Deviation (MAD)286
Skewness0.29765975
Sum5.7680312 × 108
Variance125148.72
MonotonicityNot monotonic
2024-07-13T09:45:19.844537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20001 36
 
0.1%
20202 36
 
0.1%
20343 36
 
0.1%
20377 36
 
0.1%
20417 36
 
0.1%
20750 36
 
0.1%
20832 36
 
0.1%
20855 36
 
0.1%
20877 36
 
0.1%
20914 36
 
0.1%
Other values (770) 27720
98.7%
ValueCountFrequency (%)
20001 36
0.1%
20002 36
0.1%
20003 36
0.1%
20004 36
0.1%
20005 36
0.1%
20006 36
0.1%
20007 36
0.1%
20008 36
0.1%
20009 36
0.1%
20010 36
0.1%
ValueCountFrequency (%)
21276 36
0.1%
21267 36
0.1%
21266 36
0.1%
21265 36
0.1%
21263 36
0.1%
21262 36
0.1%
21259 36
0.1%
21256 36
0.1%
21252 36
0.1%
21248 36
0.1%

periodo
Categorical

UNIFORM 

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.5 KiB
2017-01-01
 
780
2017-02-01
 
780
2017-09-01
 
780
2017-03-01
 
780
2017-04-01
 
780
Other values (31)
24180 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters280800
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-01-01
2nd row2017-02-01
3rd row2017-03-01
4th row2017-04-01
5th row2017-05-01

Common Values

ValueCountFrequency (%)
2017-01-01 780
 
2.8%
2017-02-01 780
 
2.8%
2017-09-01 780
 
2.8%
2017-03-01 780
 
2.8%
2017-04-01 780
 
2.8%
2017-05-01 780
 
2.8%
2017-06-01 780
 
2.8%
2017-07-01 780
 
2.8%
2017-08-01 780
 
2.8%
2017-10-01 780
 
2.8%
Other values (26) 20280
72.2%

Length

2024-07-13T09:45:19.968063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-01-01 780
 
2.8%
2017-02-01 780
 
2.8%
2019-03-01 780
 
2.8%
2018-09-01 780
 
2.8%
2018-10-01 780
 
2.8%
2018-11-01 780
 
2.8%
2018-12-01 780
 
2.8%
2019-01-01 780
 
2.8%
2019-02-01 780
 
2.8%
2019-04-01 780
 
2.8%
Other values (26) 20280
72.2%

Most occurring characters

ValueCountFrequency (%)
0 79560
28.3%
1 67860
24.2%
- 56160
20.0%
2 32760
11.7%
7 11700
 
4.2%
9 11700
 
4.2%
8 11700
 
4.2%
3 2340
 
0.8%
4 2340
 
0.8%
5 2340
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 280800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79560
28.3%
1 67860
24.2%
- 56160
20.0%
2 32760
11.7%
7 11700
 
4.2%
9 11700
 
4.2%
8 11700
 
4.2%
3 2340
 
0.8%
4 2340
 
0.8%
5 2340
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 280800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79560
28.3%
1 67860
24.2%
- 56160
20.0%
2 32760
11.7%
7 11700
 
4.2%
9 11700
 
4.2%
8 11700
 
4.2%
3 2340
 
0.8%
4 2340
 
0.8%
5 2340
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 280800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79560
28.3%
1 67860
24.2%
- 56160
20.0%
2 32760
11.7%
7 11700
 
4.2%
9 11700
 
4.2%
8 11700
 
4.2%
3 2340
 
0.8%
4 2340
 
0.8%
5 2340
 
0.8%

plan_precios_cuidados
Real number (ℝ)

ZEROS 

Distinct135
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99465812
Minimum0
Maximum289
Zeros27842
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:20.158956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum289
Range289
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.59233
Coefficient of variation (CV)11.654587
Kurtosis180.31647
Mean0.99465812
Median Absolute Deviation (MAD)0
Skewness12.908125
Sum27930
Variance134.38211
MonotonicityNot monotonic
2024-07-13T09:45:20.246976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27842
99.2%
93 6
 
< 0.1%
159 5
 
< 0.1%
87 5
 
< 0.1%
110 4
 
< 0.1%
65 4
 
< 0.1%
129 4
 
< 0.1%
63 4
 
< 0.1%
144 4
 
< 0.1%
123 4
 
< 0.1%
Other values (125) 198
 
0.7%
ValueCountFrequency (%)
0 27842
99.2%
9 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
29 1
 
< 0.1%
30 1
 
< 0.1%
32 2
 
< 0.1%
37 1
 
< 0.1%
42 1
 
< 0.1%
44 2
 
< 0.1%
ValueCountFrequency (%)
289 1
< 0.1%
274 1
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
213 1
< 0.1%
207 1
< 0.1%
205 1
< 0.1%
200 1
< 0.1%
199 1
< 0.1%
192 1
< 0.1%

cust_request_qty
Real number (ℝ)

ZEROS 

Distinct652
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.30345
Minimum0
Maximum756
Zeros5731
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:20.547978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median167
Q3275
95-th percentile433
Maximum756
Range756
Interquartile range (IQR)225

Descriptive statistics

Standard deviation142.31954
Coefficient of variation (CV)0.80268906
Kurtosis-0.42606118
Mean177.30345
Median Absolute Deviation (MAD)112
Skewness0.49717329
Sum4978681
Variance20254.852
MonotonicityNot monotonic
2024-07-13T09:45:20.644662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5731
 
20.4%
151 99
 
0.4%
126 91
 
0.3%
210 91
 
0.3%
168 89
 
0.3%
184 88
 
0.3%
147 88
 
0.3%
124 88
 
0.3%
208 87
 
0.3%
140 87
 
0.3%
Other values (642) 21541
76.7%
ValueCountFrequency (%)
0 5731
20.4%
1 25
 
0.1%
2 19
 
0.1%
3 16
 
0.1%
4 20
 
0.1%
5 42
 
0.1%
6 26
 
0.1%
7 33
 
0.1%
8 25
 
0.1%
9 20
 
0.1%
ValueCountFrequency (%)
756 1
< 0.1%
736 1
< 0.1%
725 1
< 0.1%
721 1
< 0.1%
718 1
< 0.1%
704 1
< 0.1%
701 1
< 0.1%
696 2
< 0.1%
695 1
< 0.1%
691 1
< 0.1%

cust_request_tn
Real number (ℝ)

ZEROS 

Distinct22141
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.880852
Minimum0
Maximum2423.7088
Zeros5731
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:20.813342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.459455
median6.22387
Q325.083377
95-th percentile190.09327
Maximum2423.7088
Range2423.7088
Interquartile range (IQR)24.623922

Descriptive statistics

Standard deviation117.3055
Coefficient of variation (CV)2.8694486
Kurtosis67.527369
Mean40.880852
Median Absolute Deviation (MAD)6.22387
Skewness6.8592699
Sum1147934.3
Variance13760.581
MonotonicityNot monotonic
2024-07-13T09:45:20.884747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5731
 
20.4%
0.00988 5
 
< 0.1%
0.00874 4
 
< 0.1%
0.0127 3
 
< 0.1%
6.3063 3
 
< 0.1%
66.41271 3
 
< 0.1%
0.48948 3
 
< 0.1%
1.51788 3
 
< 0.1%
0.01128 3
 
< 0.1%
0.02682 3
 
< 0.1%
Other values (22131) 22319
79.5%
ValueCountFrequency (%)
0 5731
20.4%
0.00089 1
 
< 0.1%
0.00092 1
 
< 0.1%
0.00218 1
 
< 0.1%
0.00223 1
 
< 0.1%
0.00268 1
 
< 0.1%
0.00294 1
 
< 0.1%
0.00295 1
 
< 0.1%
0.00301 1
 
< 0.1%
0.00311 1
 
< 0.1%
ValueCountFrequency (%)
2423.70881 1
< 0.1%
2013.36305 1
< 0.1%
2010.29987 1
< 0.1%
1945.84961 1
< 0.1%
1902.79056 1
< 0.1%
1859.88471 1
< 0.1%
1823.35962 1
< 0.1%
1782.81423 1
< 0.1%
1757.73271 1
< 0.1%
1734.24194 1
< 0.1%

tn
Real number (ℝ)

ZEROS 

Distinct22146
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.978818
Minimum0
Maximum2295.1983
Zeros5731
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:20.964176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.459985
median6.1997
Q324.879885
95-th percentile186.68862
Maximum2295.1983
Range2295.1983
Interquartile range (IQR)24.4199

Descriptive statistics

Standard deviation113.35357
Coefficient of variation (CV)2.8353406
Kurtosis65.75445
Mean39.978818
Median Absolute Deviation (MAD)6.1997
Skewness6.7677241
Sum1122605.2
Variance12849.031
MonotonicityNot monotonic
2024-07-13T09:45:21.049589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5731
 
20.4%
0.00988 5
 
< 0.1%
0.00874 4
 
< 0.1%
0.0127 3
 
< 0.1%
0.01128 3
 
< 0.1%
0.02682 3
 
< 0.1%
1.51788 3
 
< 0.1%
0.48948 3
 
< 0.1%
2.02891 3
 
< 0.1%
6.3063 3
 
< 0.1%
Other values (22136) 22319
79.5%
ValueCountFrequency (%)
0 5731
20.4%
0.00089 1
 
< 0.1%
0.00092 1
 
< 0.1%
0.00218 1
 
< 0.1%
0.00223 1
 
< 0.1%
0.00268 1
 
< 0.1%
0.00294 1
 
< 0.1%
0.00295 1
 
< 0.1%
0.00301 1
 
< 0.1%
0.00311 1
 
< 0.1%
ValueCountFrequency (%)
2295.19832 1
< 0.1%
1979.53635 1
< 0.1%
1958.59845 1
< 0.1%
1856.83534 1
< 0.1%
1813.01511 1
< 0.1%
1800.96168 1
< 0.1%
1766.81068 1
< 0.1%
1678.99318 1
< 0.1%
1660.00561 1
< 0.1%
1647.63848 1
< 0.1%

cant_periodos
Real number (ℝ)

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.652564
Minimum4
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:21.224895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q121
median36
Q336
95-th percentile36
Maximum36
Range32
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.038524
Coefficient of variation (CV)0.38525432
Kurtosis-0.43680349
Mean28.652564
Median Absolute Deviation (MAD)0
Skewness-1.1107161
Sum804564
Variance121.84901
MonotonicityNot monotonic
2024-07-13T09:45:21.451826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
36 17208
61.3%
9 1080
 
3.8%
6 792
 
2.8%
15 684
 
2.4%
16 684
 
2.4%
35 612
 
2.2%
7 576
 
2.1%
5 540
 
1.9%
10 540
 
1.9%
26 540
 
1.9%
Other values (19) 4824
 
17.2%
ValueCountFrequency (%)
4 324
 
1.2%
5 540
1.9%
6 792
2.8%
7 576
2.1%
8 468
1.7%
9 1080
3.8%
10 540
1.9%
11 144
 
0.5%
14 360
 
1.3%
15 684
2.4%
ValueCountFrequency (%)
36 17208
61.3%
35 612
 
2.2%
34 180
 
0.6%
33 72
 
0.3%
32 144
 
0.5%
31 252
 
0.9%
30 432
 
1.5%
29 180
 
0.6%
28 216
 
0.8%
27 468
 
1.7%

cat1
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size219.5 KiB
PC
15984 
HC
6732 
FOODS
5148 
REF
 
216

Length

Max length5
Median length2
Mean length2.5576923
Min length2

Characters and Unicode

Total characters71820
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHC
2nd rowHC
3rd rowHC
4th rowHC
5th rowHC

Common Values

ValueCountFrequency (%)
PC 15984
56.9%
HC 6732
24.0%
FOODS 5148
 
18.3%
REF 216
 
0.8%

Length

2024-07-13T09:45:21.702315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-13T09:45:21.768653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pc 15984
56.9%
hc 6732
24.0%
foods 5148
 
18.3%
ref 216
 
0.8%

Most occurring characters

ValueCountFrequency (%)
C 22716
31.6%
P 15984
22.3%
O 10296
14.3%
H 6732
 
9.4%
F 5364
 
7.5%
D 5148
 
7.2%
S 5148
 
7.2%
R 216
 
0.3%
E 216
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 22716
31.6%
P 15984
22.3%
O 10296
14.3%
H 6732
 
9.4%
F 5364
 
7.5%
D 5148
 
7.2%
S 5148
 
7.2%
R 216
 
0.3%
E 216
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 22716
31.6%
P 15984
22.3%
O 10296
14.3%
H 6732
 
9.4%
F 5364
 
7.5%
D 5148
 
7.2%
S 5148
 
7.2%
R 216
 
0.3%
E 216
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 22716
31.6%
P 15984
22.3%
O 10296
14.3%
H 6732
 
9.4%
F 5364
 
7.5%
D 5148
 
7.2%
S 5148
 
7.2%
R 216
 
0.3%
E 216
 
0.3%

cat2
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.5 KiB
CABELLO
7308 
DEOS
4428 
SOPAS Y CALDOS
3060 
HOGAR
2268 
PIEL2
2052 
Other values (10)
8964 

Length

Max length19
Median length14
Mean length7.5294872
Min length2

Characters and Unicode

Total characters211428
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROPA LAVADO
2nd rowROPA LAVADO
3rd rowROPA LAVADO
4th rowROPA LAVADO
5th rowROPA LAVADO

Common Values

ValueCountFrequency (%)
CABELLO 7308
26.0%
DEOS 4428
15.8%
SOPAS Y CALDOS 3060
10.9%
HOGAR 2268
 
8.1%
PIEL2 2052
 
7.3%
ROPA LAVADO 1908
 
6.8%
ADEREZOS 1764
 
6.3%
PIEL1 1692
 
6.0%
VAJILLA 1188
 
4.2%
ROPA ACONDICIONADOR 756
 
2.7%
Other values (5) 1656
 
5.9%

Length

2024-07-13T09:45:21.826626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cabello 7308
19.8%
deos 4428
12.0%
sopas 3060
8.3%
y 3060
8.3%
caldos 3060
8.3%
ropa 2736
 
7.4%
hogar 2268
 
6.1%
piel2 2052
 
5.6%
lavado 1908
 
5.2%
aderezos 1764
 
4.8%
Other values (8) 5292
14.3%

Most occurring characters

ValueCountFrequency (%)
O 30528
14.4%
A 29088
13.8%
L 26748
12.7%
E 20268
9.6%
S 16308
7.7%
D 13176
 
6.2%
C 11952
 
5.7%
P 10080
 
4.8%
8856
 
4.2%
R 8388
 
4.0%
Other values (14) 36036
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 211428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 30528
14.4%
A 29088
13.8%
L 26748
12.7%
E 20268
9.6%
S 16308
7.7%
D 13176
 
6.2%
C 11952
 
5.7%
P 10080
 
4.8%
8856
 
4.2%
R 8388
 
4.0%
Other values (14) 36036
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 211428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 30528
14.4%
A 29088
13.8%
L 26748
12.7%
E 20268
9.6%
S 16308
7.7%
D 13176
 
6.2%
C 11952
 
5.7%
P 10080
 
4.8%
8856
 
4.2%
R 8388
 
4.0%
Other values (14) 36036
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 211428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 30528
14.4%
A 29088
13.8%
L 26748
12.7%
E 20268
9.6%
S 16308
7.7%
D 13176
 
6.2%
C 11952
 
5.7%
P 10080
 
4.8%
8856
 
4.2%
R 8388
 
4.0%
Other values (14) 36036
17.0%

cat3
Text

Distinct84
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:22.050814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.9589744
Min length3

Characters and Unicode

Total characters223488
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLiquido
2nd rowLiquido
3rd rowLiquido
4th rowLiquido
5th rowLiquido
ValueCountFrequency (%)
shampoo 3276
 
9.9%
aero 2808
 
8.5%
acondicionador 2772
 
8.4%
jabon 1368
 
4.1%
liquido 1224
 
3.7%
sopas 1152
 
3.5%
mayonesa 936
 
2.8%
polvo 900
 
2.7%
noaero 828
 
2.5%
gel 792
 
2.4%
Other values (80) 17028
51.5%
2024-07-13T09:45:22.426548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 19260
 
8.6%
O 16920
 
7.6%
a 15948
 
7.1%
A 15624
 
7.0%
e 11340
 
5.1%
C 10152
 
4.5%
r 9936
 
4.4%
S 7596
 
3.4%
i 7488
 
3.4%
I 7452
 
3.3%
Other values (41) 101772
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 19260
 
8.6%
O 16920
 
7.6%
a 15948
 
7.1%
A 15624
 
7.0%
e 11340
 
5.1%
C 10152
 
4.5%
r 9936
 
4.4%
S 7596
 
3.4%
i 7488
 
3.4%
I 7452
 
3.3%
Other values (41) 101772
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 19260
 
8.6%
O 16920
 
7.6%
a 15948
 
7.1%
A 15624
 
7.0%
e 11340
 
5.1%
C 10152
 
4.5%
r 9936
 
4.4%
S 7596
 
3.4%
i 7488
 
3.4%
I 7452
 
3.3%
Other values (41) 101772
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 19260
 
8.6%
O 16920
 
7.6%
a 15948
 
7.1%
A 15624
 
7.0%
e 11340
 
5.1%
C 10152
 
4.5%
r 9936
 
4.4%
S 7596
 
3.4%
i 7488
 
3.4%
I 7452
 
3.3%
Other values (41) 101772
45.5%

brand
Categorical

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.5 KiB
NIVEA
3924 
SHAMPOO3
2952 
MAGGI
2808 
DEOS1
2808 
MUSCULO
2340 
Other values (30)
13248 

Length

Max length9
Median length8
Mean length6.3038462
Min length3

Characters and Unicode

Total characters177012
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARIEL
2nd rowARIEL
3rd rowARIEL
4th rowARIEL
5th rowARIEL

Common Values

ValueCountFrequency (%)
NIVEA 3924
14.0%
SHAMPOO3 2952
 
10.5%
MAGGI 2808
 
10.0%
DEOS1 2808
 
10.0%
MUSCULO 2340
 
8.3%
LIMPIEX 1620
 
5.8%
LANCOME 1080
 
3.8%
SHAMPOO2 1044
 
3.7%
NATURA 1008
 
3.6%
SHAMPOO1 972
 
3.5%
Other values (25) 7524
26.8%

Length

2024-07-13T09:45:22.540794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nivea 3924
14.0%
shampoo3 2952
 
10.5%
maggi 2808
 
10.0%
deos1 2808
 
10.0%
musculo 2340
 
8.3%
limpiex 1620
 
5.8%
lancome 1080
 
3.8%
shampoo2 1044
 
3.7%
natura 1008
 
3.6%
shampoo1 972
 
3.5%
Other values (25) 7524
26.8%

Most occurring characters

ValueCountFrequency (%)
O 20268
 
11.5%
A 20124
 
11.4%
M 14184
 
8.0%
I 13896
 
7.9%
E 13608
 
7.7%
S 12672
 
7.2%
N 8244
 
4.7%
P 7884
 
4.5%
L 7524
 
4.3%
G 6804
 
3.8%
Other values (25) 51804
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 20268
 
11.5%
A 20124
 
11.4%
M 14184
 
8.0%
I 13896
 
7.9%
E 13608
 
7.7%
S 12672
 
7.2%
N 8244
 
4.7%
P 7884
 
4.5%
L 7524
 
4.3%
G 6804
 
3.8%
Other values (25) 51804
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 20268
 
11.5%
A 20124
 
11.4%
M 14184
 
8.0%
I 13896
 
7.9%
E 13608
 
7.7%
S 12672
 
7.2%
N 8244
 
4.7%
P 7884
 
4.5%
L 7524
 
4.3%
G 6804
 
3.8%
Other values (25) 51804
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 20268
 
11.5%
A 20124
 
11.4%
M 14184
 
8.0%
I 13896
 
7.9%
E 13608
 
7.7%
S 12672
 
7.2%
N 8244
 
4.7%
P 7884
 
4.5%
L 7524
 
4.3%
G 6804
 
3.8%
Other values (25) 51804
29.3%

sku_size
Real number (ℝ)

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.47051
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:22.634968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q190
median220
Q3450
95-th percentile1012.5
Maximum10000
Range9999
Interquartile range (IQR)360

Descriptive statistics

Standard deviation900.09515
Coefficient of variation (CV)1.9504933
Kurtosis40.441557
Mean461.47051
Median Absolute Deviation (MAD)170
Skewness5.5003224
Sum12958092
Variance810171.27
MonotonicityNot monotonic
2024-07-13T09:45:22.897955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 2160
 
7.7%
400 1944
 
6.9%
50 1728
 
6.2%
90 1656
 
5.9%
350 1260
 
4.5%
10 1152
 
4.1%
750 1116
 
4.0%
500 1008
 
3.6%
100 1008
 
3.6%
250 900
 
3.2%
Other values (57) 14148
50.4%
ValueCountFrequency (%)
1 288
 
1.0%
2 288
 
1.0%
3 36
 
0.1%
4 288
 
1.0%
5 540
1.9%
6 144
 
0.5%
8 216
 
0.8%
10 1152
4.1%
12 252
 
0.9%
15 252
 
0.9%
ValueCountFrequency (%)
10000 72
 
0.3%
5000 396
1.4%
4000 36
 
0.1%
3000 720
2.6%
2000 72
 
0.3%
1400 72
 
0.3%
1250 36
 
0.1%
1000 468
1.7%
950 108
 
0.4%
930 504
1.8%
Distinct427
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size219.5 KiB
2024-07-13T09:45:23.148028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length36
Median length27
Mean length11.774359
Min length2

Characters and Unicode

Total characters330624
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgenoma
2nd rowgenoma
3rd rowgenoma
4th rowgenoma
5th rowgenoma
ValueCountFrequency (%)
sabor 3384
 
6.3%
1 1476
 
2.7%
sopa 1080
 
2.0%
2 1044
 
1.9%
3 864
 
1.6%
aroma 864
 
1.6%
antibacterial 792
 
1.5%
crema 792
 
1.5%
maquina 720
 
1.3%
regular 720
 
1.3%
Other values (362) 42084
78.2%
2024-07-13T09:45:23.564454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 39420
 
11.9%
o 27720
 
8.4%
25776
 
7.8%
i 24840
 
7.5%
e 21816
 
6.6%
r 20844
 
6.3%
n 19044
 
5.8%
l 15480
 
4.7%
t 13500
 
4.1%
s 12924
 
3.9%
Other values (57) 109260
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 330624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 39420
 
11.9%
o 27720
 
8.4%
25776
 
7.8%
i 24840
 
7.5%
e 21816
 
6.6%
r 20844
 
6.3%
n 19044
 
5.8%
l 15480
 
4.7%
t 13500
 
4.1%
s 12924
 
3.9%
Other values (57) 109260
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 330624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 39420
 
11.9%
o 27720
 
8.4%
25776
 
7.8%
i 24840
 
7.5%
e 21816
 
6.6%
r 20844
 
6.3%
n 19044
 
5.8%
l 15480
 
4.7%
t 13500
 
4.1%
s 12924
 
3.9%
Other values (57) 109260
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 330624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 39420
 
11.9%
o 27720
 
8.4%
25776
 
7.8%
i 24840
 
7.5%
e 21816
 
6.6%
r 20844
 
6.3%
n 19044
 
5.8%
l 15480
 
4.7%
t 13500
 
4.1%
s 12924
 
3.9%
Other values (57) 109260
33.0%

Interactions

2024-07-13T09:45:18.625042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:14.283848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.061848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.921333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.691574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.335541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.901437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.786883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:14.475931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.210927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.178271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.755265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.438750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.053472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.910906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:14.551780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.434445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.240689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.817210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.509464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.121339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:19.022007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:14.654822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.550927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.303849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.871770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.589314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.248723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:19.089562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:14.793304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.669691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.394195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.947161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.648860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.338982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:19.149924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:14.894095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.712869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.469268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.048788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.721962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.417524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:19.253395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.001341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:15.789880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:16.567852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.187792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:17.789155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-13T09:45:18.477335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-13T09:45:19.347787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-13T09:45:19.588479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

product_idperiodoplan_precios_cuidadoscust_request_qtycust_request_tntncant_periodoscat1cat2cat3brandsku_sizedescripcion
0200012017-01-010.0479.0937.72717934.7722236HCROPA LAVADOLiquidoARIEL3000genoma
1200012017-02-010.0432.0833.72187798.0162036HCROPA LAVADOLiquidoARIEL3000genoma
2200012017-03-010.0509.01330.746971303.3577136HCROPA LAVADOLiquidoARIEL3000genoma
3200012017-04-010.0279.01132.944301069.9613036HCROPA LAVADOLiquidoARIEL3000genoma
4200012017-05-010.0701.01550.689361502.2013236HCROPA LAVADOLiquidoARIEL3000genoma
5200012017-06-010.0570.01575.828911520.0653936HCROPA LAVADOLiquidoARIEL3000genoma
6200012017-07-010.0381.01086.471011030.6739136HCROPA LAVADOLiquidoARIEL3000genoma
7200012017-08-010.0643.01289.668691267.3946236HCROPA LAVADOLiquidoARIEL3000genoma
8200012017-09-010.0381.01356.961031316.9460436HCROPA LAVADOLiquidoARIEL3000genoma
9200012017-10-010.0273.01441.602471439.7556336HCROPA LAVADOLiquidoARIEL3000genoma
product_idperiodoplan_precios_cuidadoscust_request_qtycust_request_tntncant_periodoscat1cat2cat3brandsku_sizedescripcion
28070212142019-03-010.00.00.000000.000004PCDEOSRollOnNIVEA50Aroma 14
28071212142019-04-010.00.00.000000.000004PCDEOSRollOnNIVEA50Aroma 14
28072212142019-05-010.00.00.000000.000004PCDEOSRollOnNIVEA50Aroma 14
28073212142019-06-010.00.00.000000.000004PCDEOSRollOnNIVEA50Aroma 14
28074212142019-07-010.00.00.000000.000004PCDEOSRollOnNIVEA50Aroma 14
28075212142019-08-010.00.00.000000.000004PCDEOSRollOnNIVEA50Aroma 14
28076212142019-09-010.03.00.342500.342504PCDEOSRollOnNIVEA50Aroma 14
28077212142019-10-010.046.00.217350.217354PCDEOSRollOnNIVEA50Aroma 14
28078212142019-11-010.088.00.840120.840124PCDEOSRollOnNIVEA50Aroma 14
28079212142019-12-010.055.00.244280.244284PCDEOSRollOnNIVEA50Aroma 14